TensorFlow-Input-Pipeline | TensorFlow Input Pipeline Examples | Dataset library

by zzw922cn Python Version: Current License: No License

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kandi X-RAY | TensorFlow-Input-Pipeline Summary

TensorFlow-Input-Pipeline is a Python library typically used in Artificial Intelligence, Dataset, Tensorflow applications. TensorFlow-Input-Pipeline has no bugs, it has no vulnerabilities and it has low support. However TensorFlow-Input-Pipeline build file is not available. You can download it from GitHub.

TensorFlow Input Pipeline Examples based on multi-thread and FIFOQueue

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TensorFlow-Input-Pipeline has a low active ecosystem.

It has 54 star(s) with 18 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

TensorFlow-Input-Pipeline has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of TensorFlow-Input-Pipeline is current.

Quality

TensorFlow-Input-Pipeline has 0 bugs and 0 code smells.

Security

TensorFlow-Input-Pipeline has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

TensorFlow-Input-Pipeline code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

TensorFlow-Input-Pipeline does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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TensorFlow-Input-Pipeline releases are not available. You will need to build from source code and install.

TensorFlow-Input-Pipeline has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

TensorFlow-Input-Pipeline saves you 75 person hours of effort in developing the same functionality from scratch.

It has 193 lines of code, 5 functions and 2 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed TensorFlow-Input-Pipeline and discovered the below as its top functions. This is intended to give you an instant insight into TensorFlow-Input-Pipeline implemented functionality, and help decide if they suit your requirements.

Write data to file .
Reads examples from a tensorflow file .
Create a tf . train . Feature .
Create a tf . train . Feature .
Initialize path .

Get all kandi verified functions for this library.

TensorFlow-Input-Pipeline Key Features

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TensorFlow-Input-Pipeline Examples and Code Snippets

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Community Discussions

Trending Discussions on TensorFlow-Input-Pipeline

TensorFlow TypeError: Value passed to parameter input has DataType uint8 not in list of allowed values: float16, float32

Correct way of doing data augmentation in TensorFlow with the dataset api?

QUESTION

TensorFlow TypeError: Value passed to parameter input has DataType uint8 not in list of allowed values: float16, float32

Asked 2019-Dec-29 at 06:03

I am trying to get a simple CNN to train for the past 3 days.

First, I have setup an input pipeline/queue configuration that reads images from a directory tree and prepares batches.

I got the code for this at this link. So, I now have train_image_batch and train_label_batch that I need to feed to my CNN.

...

ANSWER

Answered 2017-Jun-29 at 10:55

The image from your input pipeline is of type 'uint8', you need to type cast it to 'float32', You can do this after the image jpeg decoder:

Source https://stackoverflow.com/questions/44822999

QUESTION

Correct way of doing data augmentation in TensorFlow with the dataset api?

Asked 2017-Dec-13 at 13:23

So, I've been playing around with the TensorFlow dataset API for loading images, and segmentation masks (for a semantic segmentation project), I would like to be able to generate batches of images and masks, with each image having randomly gone through any combination of pre-processing functions like brightness changes, contrast changes, cropping, saturation changes etc. So, the first image in my batch may have no pre-processing, second may have saturation changes, third may have brightness and saturation and so on.

I tried the following:

...

ANSWER

Answered 2017-Dec-13 at 13:23

So the problem was indeed that the control flow with the if statements are with Python variables, and are only executed once when the graph is created, to do what I want to do, I had to define a placeholder that contains the boolean values of whether to apply a function or not (and feed in a new boolean tensor per iteration to change the augmentation), and control flow is handled by tf.cond. I pushed the new code to the GitHub link I posted in the question above if anyone is interested.

Source https://stackoverflow.com/questions/47781283

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install TensorFlow-Input-Pipeline

You can download it from GitHub.
You can use TensorFlow-Input-Pipeline like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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